In the matching tasks which form an integral part of all types of tracking and geometrical vision, there are invariably priors available on the absolute and/or relative image locations of features of interest. Usually, these priors are used post-hoc in the process of resolving feature matches and obtaining final scene estimates, via ‘first get candidate matches, then resolve’ consensus algorithms such as RANSAC. In this paper we show that the dramatically different approach of using priors dynamically to guide a feature by feature matching search can achieve global matching with much fewer image processing operations and lower overall computational cost. Essentially, we put image processing into the loop of the search for global consensus. In particular, our approach is able to cope with significant image ambiguity thanks to a dynamic mixture of Gaussians treatment. In our fully Bayesian algorithm, the choice of the most efficient search action at each step is guided intuitively and rigorously by expected Shannon information gain. We demonstrate the algorithm in feature matching as part of a sequential SLAM system for 3D camera tracking. Robust, real-time matching can be achieved even in the previously unmanageable case of jerky, rapid motion necessitating weak motion modelling and large search regions.
KeywordsMutual Information Template Match Search Region True Match Image Processing Operation
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- 1.Clemente, L.A., Davison, A.J., Reid, I.D., Neira, J., Tardós, J.D.: Mapping large loops with a single hand-held camera. In: Proceedings of Robotics: Science and Systems (RSS) (2007)Google Scholar
- 2.Cummins, M., Newman, P.: Probabilistic appearance based navigation and loop closing. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (2007)Google Scholar
- 3.Davison, A.J.: Active search for real-time vision. In: Proceedings of the International Conference on Computer Vision (ICCV) (2005)Google Scholar
- 5.Davison, A.J., Murray, D.W.: Mobile robot localisation using active vision. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407. Springer, Heidelberg (1998)Google Scholar
- 7.Grimson, W.E.L.: Object Recognition by Computer: The Role of Geometric Constraints. MIT Press, Cambridge (1990)Google Scholar
- 10.Manyika, J.: An Information-Theoretic Approach to Data Fusion and Sensor Management. PhD thesis, University of Oxford (1993)Google Scholar
- 13.Williams, B., Klein, G., Reid, I.: Real-time SLAM relocalisation. In: Proceedings of the International Conference on Computer Vision (ICCV) (2007)Google Scholar